Senior Machine Learning Engineer, Applied AI & Data

GitKraken is the developer experience platform of choice for over 40 million developers globally. They are seeking a Senior Machine Learning Engineer to take ideas from concept to production, focusing on identifying high-value opportunities and executing end-to-end data solutions.

Responsibilities:

• Identify high-value opportunities from product, customer, and operational data
• Evaluate ambiguous ideas quickly and determine what is feasible, useful, and worth shipping
• Identify high-value opportunities from product, customer, and operational data
• Build practical 80/20 solutions that create leverage quickly, then refine them based on traction
• Own end-to-end execution across data exploration, modeling, experimentation, backend integration, and productization
• Partner with engineering, product, design, and leadership to turn rough ideas into shipped capabilities
• Use ML, analytics, heuristics, and automation pragmatically rather than forcing a model where one is not needed
• Define success metrics, instrument outcomes, and improve solutions based on real-world usage
• Help shape how GitKraken uses AI and data to improve developer workflows, team velocity, and product experience

Requirements:

• Deep experience in machine learning, applied AI, or a similarly hands-on product data role at a Senior level
• A track record of shipping data or ML-powered capabilities into real products or operational workflows
• Comfort moving from messy problem statements to practical execution without a lot of structure
• Ability to work across the stack, not just in notebooks
• Strong product judgment and a bias toward simple solutions that deliver measurable value
• Experience deciding whether a problem is best solved with ML, rules, analytics, automation, or workflow design
• Ability to balance speed and rigor, including knowing when 'good enough to learn' is the right answer
• Strong communication skills and the ability to explain tradeoffs clearly to technical and non-technical partners
• Ownership mindset: you don't wait for perfect specs, and you follow through from idea to impact
• You've built and shipped data or ML-powered features, not just analyses
• You can prototype quickly and are comfortable refining after launch
• You know how to avoid getting buried in edge cases before the core value is proven
• You like working in a company with a bias toward action, accountability, and high ownership
• You want your work to directly influence product direction and business outcomes